#Criando seleção com filtro

## Warning in readRDS(dest): lzma decoding result 10
## Warning: package 'funModeling' is not available for this version of R
## 
## A version of this package for your version of R might be available elsewhere,
## see the ideas at
## https://cran.r-project.org/doc/manuals/r-patched/R-admin.html#Installing-packages
## Warning: 'BiocManager' not available.  Could not check Bioconductor.
## 
## Please use `install.packages('BiocManager')` and then retry.
## Warning in p_install(package, character.only = TRUE, ...):
## Warning in library(package, lib.loc = lib.loc, character.only = TRUE,
## logical.return = TRUE, : there is no package called 'funModeling'
## Warning in pacman::p_load(crosstalk, dplyr, DT, plotly, ade4, car, mboost, : Failed to install/load:
## funModeling
##     observado  previsao
## 2         1.8  1.612801
## 3         1.6  1.628995
## 6         2.0  1.665639
## 7         1.5  1.624189
## 8         1.5  1.699835
## 12        1.4  1.628120
## 13        1.7  1.787987
## 16        1.6  1.645514
## 18        1.8  1.668377
## 25        1.7  1.700882
## 28        2.0  1.672643
## 32        1.6  1.665695
## 33        1.5  1.616371
## 40        1.6  1.639984
## 41        1.6  1.643969
## 47        1.8  1.657652
## 49        1.8  1.609474
## 51        1.8  1.632020
## 54        1.5  1.610021
## 57        1.7  1.627929
## 65        1.6  1.653578
## 68        2.5  5.432946
## 69        1.8  1.608522
## 72        1.6  1.667171
## 73        1.6  1.607926
## 74        2.1  2.060161
## 79        3.2  3.533053
## 80        1.7  1.698478
## 83        1.5  1.644211
## 84        1.7  1.613960
## 87        2.0  2.072357
## 90        1.8  1.633803
## 91        1.7  1.660387
## 94        1.5  1.625388
## 100       1.4  1.701618
## 104       1.4  1.683280
## 118       1.4  1.694352
## 121       1.6  1.806402
## 123       1.7  1.665142
## 128       1.6  1.660173
## 131       1.7  1.603065
## 132       3.3 -1.192398
## 150       1.6  1.653258
## 151       1.6  1.642593
## 159       1.3  1.637870
## 160       2.4  1.721689
## 167       1.5  1.628467
## 169       1.7  1.615920
## 173       1.6  1.617061
## 174       1.7  1.652056
## 176       1.8  1.651589
## 180       1.5  1.639594
## 182       1.5  1.699076
## 184       1.3  1.625135
## [1] "Erro Quadrático Médio (MSE): 0.567375196003104"
## Warning in nominalTrainWorkflow(x = x, y = y, wts = weights, info = trainInfo,
## : There were missing values in resampled performance measures.
## Aggregating results
## Selecting tuning parameters
## Fitting alpha = 0.479, lambda = 0.0436 on full training set

##             Length Class      Mode     
## a0          100    -none-     numeric  
## beta        700    dgCMatrix  S4       
## df          100    -none-     numeric  
## dim           2    -none-     numeric  
## lambda      100    -none-     numeric  
## dev.ratio   100    -none-     numeric  
## nulldev       1    -none-     numeric  
## npasses       1    -none-     numeric  
## jerr          1    -none-     numeric  
## offset        1    -none-     logical  
## call          5    -none-     call     
## nobs          1    -none-     numeric  
## lambdaOpt     1    -none-     numeric  
## xNames        7    -none-     character
## problemType   1    -none-     character
## tuneValue     2    data.frame list     
## obsLevels     1    -none-     logical  
## param         0    -none-     list

## 8 x 1 sparse Matrix of class "dgCMatrix"
##                       s1
## (Intercept) 1.648431e+00
## pop         .           
## pib         1.843440e-11
## vab         .           
## icms        1.217506e-07
## ipi         3.012624e-05
## ipva        .           
## pop_ocu_per .
## Warning in nominalTrainWorkflow(x = x, y = y, wts = weights, info = trainInfo,
## : There were missing values in resampled performance measures.
## Aggregating results
## Selecting tuning parameters
## Fitting cp = 0.0338 on full training set

## Call:
## (function (formula, data, weights, subset, na.action = na.rpart, 
##     method, model = FALSE, x = FALSE, y = TRUE, parms, control, 
##     cost, ...) 
## {
##     Call <- match.call()
##     if (is.data.frame(model)) {
##         m <- model
##         model <- FALSE
##     }
##     else {
##         indx <- match(c("formula", "data", "weights", "subset"), 
##             names(Call), nomatch = 0)
##         if (indx[1] == 0) 
##             stop("a 'formula' argument is required")
##         temp <- Call[c(1, indx)]
##         temp$na.action <- na.action
##         temp[[1]] <- quote(stats::model.frame)
##         m <- eval.parent(temp)
##     }
##     Terms <- attr(m, "terms")
##     if (any(attr(Terms, "order") > 1)) 
##         stop("Trees cannot handle interaction terms")
##     Y <- model.response(m)
##     wt <- model.weights(m)
##     if (any(wt < 0)) 
##         stop("negative weights not allowed")
##     if (!length(wt)) 
##         wt <- rep(1, nrow(m))
##     offset <- model.offset(m)
##     X <- rpart.matrix(m)
##     nobs <- nrow(X)
##     nvar <- ncol(X)
##     if (missing(method)) {
##         method <- if (is.factor(Y) || is.character(Y)) 
##             "class"
##         else if (inherits(Y, "Surv")) 
##             "exp"
##         else if (is.matrix(Y)) 
##             "poisson"
##         else "anova"
##     }
##     if (is.list(method)) {
##         mlist <- method
##         method <- "user"
##         init <- if (missing(parms)) 
##             mlist$init(Y, offset, wt = wt)
##         else mlist$init(Y, offset, parms, wt)
##         keep <- rpartcallback(mlist, nobs, init)
##         method.int <- 4
##         parms <- init$parms
##     }
##     else {
##         method.int <- pmatch(method, c("anova", "poisson", "class", 
##             "exp"))
##         if (is.na(method.int)) 
##             stop("Invalid method")
##         method <- c("anova", "poisson", "class", "exp")[method.int]
##         if (method.int == 4) 
##             method.int <- 2
##         init <- if (missing(parms)) 
##             get(paste("rpart", method, sep = "."), envir = environment())(Y, 
##                 offset, , wt)
##         else get(paste("rpart", method, sep = "."), envir = environment())(Y, 
##             offset, parms, wt)
##         ns <- asNamespace("rpart")
##         if (!is.null(init$print)) 
##             environment(init$print) <- ns
##         if (!is.null(init$summary)) 
##             environment(init$summary) <- ns
##         if (!is.null(init$text)) 
##             environment(init$text) <- ns
##     }
##     Y <- init$y
##     xlevels <- .getXlevels(Terms, m)
##     cats <- rep(0, ncol(X))
##     if (!is.null(xlevels)) {
##         indx <- match(names(xlevels), colnames(X), nomatch = 0)
##         cats[indx] <- (unlist(lapply(xlevels, length)))[indx > 
##             0]
##     }
##     extraArgs <- list(...)
##     if (length(extraArgs)) {
##         controlargs <- names(formals(rpart.control))
##         indx <- match(names(extraArgs), controlargs, nomatch = 0)
##         if (any(indx == 0)) 
##             stop(gettextf("Argument %s not matched", names(extraArgs)[indx == 
##                 0]), domain = NA)
##     }
##     controls <- rpart.control(...)
##     if (!missing(control)) 
##         controls[names(control)] <- control
##     xval <- controls$xval
##     if (is.null(xval) || (length(xval) == 1 && xval == 0) || 
##         method == "user") {
##         xgroups <- 0
##         xval <- 0
##     }
##     else if (length(xval) == 1) {
##         xgroups <- sample(rep(1:xval, length.out = nobs), nobs, 
##             replace = FALSE)
##     }
##     else if (length(xval) == nobs) {
##         xgroups <- xval
##         xval <- length(unique(xgroups))
##     }
##     else {
##         if (!is.null(attr(m, "na.action"))) {
##             temp <- as.integer(attr(m, "na.action"))
##             xval <- xval[-temp]
##             if (length(xval) == nobs) {
##                 xgroups <- xval
##                 xval <- length(unique(xgroups))
##             }
##             else stop("Wrong length for 'xval'")
##         }
##         else stop("Wrong length for 'xval'")
##     }
##     if (missing(cost)) 
##         cost <- rep(1, nvar)
##     else {
##         if (length(cost) != nvar) 
##             stop("Cost vector is the wrong length")
##         if (any(cost <= 0)) 
##             stop("Cost vector must be positive")
##     }
##     tfun <- function(x) if (is.matrix(x)) 
##         rep(is.ordered(x), ncol(x))
##     else is.ordered(x)
##     labs <- sub("^`(.*)`$", "\\1", attr(Terms, "term.labels"))
##     isord <- unlist(lapply(m[labs], tfun))
##     storage.mode(X) <- "double"
##     storage.mode(wt) <- "double"
##     temp <- as.double(unlist(init$parms))
##     if (!length(temp)) 
##         temp <- 0
##     rpfit <- .Call(C_rpart, ncat = as.integer(cats * !isord), 
##         method = as.integer(method.int), as.double(unlist(controls)), 
##         temp, as.integer(xval), as.integer(xgroups), as.double(t(init$y)), 
##         X, wt, as.integer(init$numy), as.double(cost))
##     nsplit <- nrow(rpfit$isplit)
##     ncat <- if (!is.null(rpfit$csplit)) 
##         nrow(rpfit$csplit)
##     else 0
##     if (nsplit == 0) 
##         xval <- 0
##     numcp <- ncol(rpfit$cptable)
##     temp <- if (nrow(rpfit$cptable) == 3) 
##         c("CP", "nsplit", "rel error")
##     else c("CP", "nsplit", "rel error", "xerror", "xstd")
##     dimnames(rpfit$cptable) <- list(temp, 1:numcp)
##     tname <- c("<leaf>", colnames(X))
##     splits <- matrix(c(rpfit$isplit[, 2:3], rpfit$dsplit), ncol = 5, 
##         dimnames = list(tname[rpfit$isplit[, 1] + 1], c("count", 
##             "ncat", "improve", "index", "adj")))
##     index <- rpfit$inode[, 2]
##     nadd <- sum(isord[rpfit$isplit[, 1]])
##     if (nadd > 0) {
##         newc <- matrix(0, nadd, max(cats))
##         cvar <- rpfit$isplit[, 1]
##         indx <- isord[cvar]
##         cdir <- splits[indx, 2]
##         ccut <- floor(splits[indx, 4])
##         splits[indx, 2] <- cats[cvar[indx]]
##         splits[indx, 4] <- ncat + 1:nadd
##         for (i in 1:nadd) {
##             newc[i, 1:(cats[(cvar[indx])[i]])] <- -as.integer(cdir[i])
##             newc[i, 1:ccut[i]] <- as.integer(cdir[i])
##         }
##         catmat <- if (ncat == 0) 
##             newc
##         else {
##             cs <- rpfit$csplit
##             ncs <- ncol(cs)
##             ncc <- ncol(newc)
##             if (ncs < ncc) 
##                 cs <- cbind(cs, matrix(0, nrow(cs), ncc - ncs))
##             rbind(cs, newc)
##         }
##         ncat <- ncat + nadd
##     }
##     else catmat <- rpfit$csplit
##     if (nsplit == 0) {
##         frame <- data.frame(row.names = 1, var = "<leaf>", n = rpfit$inode[, 
##             5], wt = rpfit$dnode[, 3], dev = rpfit$dnode[, 1], 
##             yval = rpfit$dnode[, 4], complexity = rpfit$dnode[, 
##                 2], ncompete = 0, nsurrogate = 0)
##     }
##     else {
##         temp <- ifelse(index == 0, 1, index)
##         svar <- ifelse(index == 0, 0, rpfit$isplit[temp, 1])
##         frame <- data.frame(row.names = rpfit$inode[, 1], var = tname[svar + 
##             1], n = rpfit$inode[, 5], wt = rpfit$dnode[, 3], 
##             dev = rpfit$dnode[, 1], yval = rpfit$dnode[, 4], 
##             complexity = rpfit$dnode[, 2], ncompete = pmax(0, 
##                 rpfit$inode[, 3] - 1), nsurrogate = rpfit$inode[, 
##                 4])
##     }
##     if (method.int == 3) {
##         numclass <- init$numresp - 2
##         nodeprob <- rpfit$dnode[, numclass + 5]/sum(wt)
##         temp <- pmax(1, init$counts)
##         temp <- rpfit$dnode[, 4 + (1:numclass)] %*% diag(init$parms$prior/temp)
##         yprob <- temp/rowSums(temp)
##         yval2 <- matrix(rpfit$dnode[, 4 + (0:numclass)], ncol = numclass + 
##             1)
##         frame$yval2 <- cbind(yval2, yprob, nodeprob)
##     }
##     else if (init$numresp > 1) 
##         frame$yval2 <- rpfit$dnode[, -(1:3), drop = FALSE]
##     if (is.null(init$summary)) 
##         stop("Initialization routine is missing the 'summary' function")
##     functions <- if (is.null(init$print)) 
##         list(summary = init$summary)
##     else list(summary = init$summary, print = init$print)
##     if (!is.null(init$text)) 
##         functions <- c(functions, list(text = init$text))
##     if (method == "user") 
##         functions <- c(functions, mlist)
##     where <- rpfit$which
##     names(where) <- row.names(m)
##     ans <- list(frame = frame, where = where, call = Call, terms = Terms, 
##         cptable = t(rpfit$cptable), method = method, parms = init$parms, 
##         control = controls, functions = functions, numresp = init$numresp)
##     if (nsplit) 
##         ans$splits = splits
##     if (ncat > 0) 
##         ans$csplit <- catmat + 2
##     if (nsplit) 
##         ans$variable.importance <- importance(ans)
##     if (model) {
##         ans$model <- m
##         if (missing(y)) 
##             y <- FALSE
##     }
##     if (y) 
##         ans$y <- Y
##     if (x) {
##         ans$x <- X
##         ans$wt <- wt
##     }
##     ans$ordered <- isord
##     if (!is.null(attr(m, "na.action"))) 
##         ans$na.action <- attr(m, "na.action")
##     if (!is.null(xlevels)) 
##         attr(ans, "xlevels") <- xlevels
##     if (method == "class") 
##         attr(ans, "ylevels") <- init$ylevels
##     class(ans) <- "rpart"
##     ans
## })(formula = .outcome ~ ., data = list(c(100346, 25065, 37082, 
## 22984, 22870, 11226, 74822, 14510, 12122, 76687, 12765, 60880, 
## 38378, 48767, 39983, 8987, 7488, 13190, 58919, 208944, 28904, 
## 11018, 5747, 158899, 18900, 8572, 19609, 13025, 84395, 365278, 
## 14368, 11891, 21815, 18268, 12560, 24173, 37375, 69292, 31766, 
## 22247, 12170, 22618, 33184, 15546, 140577, 30751, 84699, 19241, 
## 12247, 26672, 23645, 4543, 4966, 26308, 26900, 17006, 11644, 
## 17228, 16011, 11485, 15431, 21460, 18252, 13224, 16318, 40589, 
## 56198, 25472, 21776, 11220, 15470, 11270, 63294, 32573, 393115, 
## 23935, 15152, 69969, 63500, 7600, 26456, 22106, 28894, 334376, 
## 67735, 36901, 354317, 11305, 15101, 26175, 20646, 47616, 23628, 
## 9764, 11068, 61249, 15862, 26890, 15558, 12650, 109897, 14562, 
## 42100, 14223, 11865, 16069, 60042, 21398, 21586, 34021, 34056, 
## 114079, 18085, 86915, 46361, 6021, 65647, 28704, 12859, 26106, 
## 29127, 10206, 52802, 46164, 15243, 8256, 18661, 9553, 20954, 
## 139583), c(1725530040, 254433749, 246012396, 171957774, 223576627, 
## 118494207, 1007778325, 129193317, 80071216, 1883328580, 87858630, 
## 722013305, 276756188, 503785177, 327251686, 144883318, 55555609, 
## 103451021, 406916743, 9758236662, 486379337, 93547528, 50248156, 
## 1850647501, 153617568, 129829606, 154574536, 78625308, 1447601312, 
## 7147526418, 94875081, 88575588, 199848143, 149792966, 119516551, 
## 263969197, 497295327, 956663959, 268382301, 191063979, 112514665, 
## 170092843, 418863027, 115759710, 2441308810, 456747907, 1124665213, 
## 161992547, 89714876, 249443705, 167130866, 36207443, 55403615, 
## 215492010, 1563044592, 132513399, 109198668, 124670220, 183360566, 
## 100427145, 104187918, 320808438, 247578348, 115729415, 140251656, 
## 446585824, 776093813, 238820003, 122157971, 87524952, 151214926, 
## 81820138, 657888056, 528191186, 5533876468, 321655345, 173757567, 
## 666745141, 800303528, 68717743, 190163512, 166902596, 224562554, 
## 4190092519, 688562864, 1329912205, 6686658334, 83988256, 159162571, 
## 179830993, 181657839, 448647830, 266502475, 125396696, 67495839, 
## 858917924, 127168967, 199171392, 108750766, 90615401, 1511867664, 
## 97983803, 660076031, 102556274, 85905281, 96619312, 1085366267, 
## 160376964, 231879079, 271435682, 384206259, 1181132228, 213004395, 
## 1373040468, 490988356, 43970016, 753840601, 265325241, 162555722, 
## 173317009, 280500896, 68548740, 681732740, 654978750, 142051785, 
## 78738813, 215607654, 68372803, 160055642, 3718817024), c(656390983, 
## 83058312, 60468092, 47936240, 54176074, 19820377, 542244942, 
## 25321515, 18405707, 511398132, 19780174, 280700841, 64331238, 
## 151358767, 87849926, 20411079, 12737860, 24536154, 98875695, 
## 3737740286, 45771274, 17670743, 13932436, 895966678, 55543895, 
## 29986202, 44081240, 13947979, 620429564, 3839255608, 17748496, 
## 22133022, 62673519, 33220844, 27031067, 102358182, 204199679, 
## 329322973, 76398156, 54779483, 33804131, 48997413, 168202548, 
## 31643252, 1150729004, 110636358, 536409265, 33057414, 22652537, 
## 97655864, 39973382, 7856614, 12177633, 54149398, 347202101, 29500907, 
## 22950226, 34331916, 38463661, 16824806, 24566718, 82369842, 65314214, 
## 20584297, 24271695, 180915672, 375196597, 82318734, 20986459, 
## 18120373, 58076334, 18545287, 192977143, 173844762, 2849259015, 
## 60375043, 34222248, 263996287, 419643739, 13036578, 47294417, 
## 50318389, 68384013, 1932782631, 265147648, 243063483, 2960723368, 
## 21387963, 33332696, 43339918, 50796743, 140027001, 66377057, 
## 39555913, 12383244, 447705972, 29929054, 58978805, 25821538, 
## 22328772, 661367763, 20889019, 118306474, 29041006, 21105857, 
## 16939256, 160053769, 44238346, 99389556, 88972910, 130311800, 
## 468049165, 49423963, 691459819, 138488056, 9284260, 356037259, 
## 106817596, 32587156, 40800382, 88584368, 16488295, 306009949, 
## 272313410, 52006041, 27278430, 46973235, 15190000, 54715746, 
## 1489988724), c(205327.94, 26285.21, 21192.56, 20662.94, 31107.66, 
## 15184.68, 87801.63, 20137.98, 21177.12, 160308.46, 16413.15, 
## 51624.99, 25987.95, 43608.99, 36364.88, 24030.24, 29051.53, 28128.57, 
## 38201.54, 1253816.42, 38077.36, 26597.41, 24851.7, 24851.7, 21348.37, 
## 27201.12, 23898.83, 16223.84, 116117.13, 536035.02, 17424.6, 
## 23139.23, 21609.62, 22588.99, 27608.49, 25380.22, 47726.5, 89629.59, 
## 26818.43, 23313.91, 28265.1, 20327.55, 85619.6, 17364.45, 202846.33, 
## 54799.93, 94618.46, 19008.16, 23671.65, 0, 18310.85, 20396.59, 
## 45589.13, 25099.22, 189121.23, 18557.23, 22810.01, 17325.3, 35161.42, 
## 16273.79, 16297.23, 16297.23, 35880.26, 23386.49, 18050.68, 34829.85, 
## 62734.83, 25233.95, 20332.44, 20898.14, 16139.09, 21357.44, 55825.63, 
## 47666.34, 608979.03, 26326.16, 15455.67, 40390.31, 60102.21, 
## 22105.31, 21168.66, 19735.35, 21994.96, 383196.34, 55423, 84990.55, 
## 493916.22, 16764.25, 30122.11, 19279.42, 20043.94, 45095.03, 
## 63235.37, 23159.69, 34009.98, 69049.75, 17919.04, 19099.07, 17248.53, 
## 18787.42, 105552.43, 22524.85, 27766.37, 17976.55, 18039.87, 
## 16502.54, 45298.25, 26084.73, 21789.66, 25301.45, 29154.81, 88321.43, 
## 21354.07, 112054.41, 90898.45, 27315.34, 52306.11, 25647.65, 
## 16562.04, 44020.37, 23554.19, 29365.02, 60708.58, 53025.58, 24209.26, 
## 18255.98, 26409.04, 16775.35, 17776.34, 377043.29), c(823.29, 
## 107.2, 85.71, 83.59, 125.91, 61.45, 355.28, 81.45, 85.7, 647.34, 
## 66.39, 209.56, 105.16, 178.25, 147.15, 99.02, 117.57, 113.83, 
## 154.6, 5021.78, 154.02, 107.6, 100.54, 100.54, 87.1, 104.55, 
## 96.65, 65.69, 469.66, 2168.11, 70.94, 93.66, 87.45, 91.41, 111.76, 
## 103.39, 194.45, 363.75, 108.51, 94.52, 114.42, 82.23, 346.62, 
## 70.26, 822.02, 221.8, 383.2, 76.89, 95.75, 0, 74.07, 82.51, 184.61, 
## 101.52, 763.21, 75.44, 92.31, 70.11, 142.28, 65.86, 65.93, 65.93, 
## 150.06, 94.61, 73.01, 141.69, 253.8, 102.08, 82.28, 84.56, 65.32, 
## 86.45, 225.81, 192.85, 2464.72, 106.48, 62.51, 163.37, 243.13, 
## 91.41, 85.84, 79.85, 88.98, 1550.27, 224.2, 345.81, 2003.6, 67.83, 
## 121.95, 78.01, 81.07, 182.5, 255.95, 95.02, 137.69, 279.29, 72.48, 
## 77.27, 69.8, 76.03, 426.96, 91.18, 112.32, 72.93, 73, 66.77, 
## 183.22, 107.03, 88.15, 102.37, 117.95, 357.29, 86.4, 453.33, 
## 367.87, 110.55, 211.58, 103.73, 67.01, 178.21, 95.29, 118.86, 
## 245.58, 214.53, 97.96, 73.85, 108.34, 67.9, 71.89, 1531.72), 
##     c(36815.55, 6841.95, 3939.39, 4097.53, 3324.87, 2160.16, 
##     39263.23, 2137.24, 1456.08, 28180.13, 2095.89, 24346.14, 
##     5390.34, 9883.51, 8418.68, 1863.71, 854.15, 2518.67, 8690.43, 
##     80203.6, 5311.85, 1975.02, 1818.66, 1818.66, 4605.92, 2355.32, 
##     4140.65, 1206.93, 43476.53, 251533.75, 1829.01, 1578.47, 
##     7114.56, 2726.04, 2083.09, 8964.54, 11903.93, 18919.66, 5729.18, 
##     6155.57, 2906.33, 4492.67, 8712.27, 4020.44, 87058.74, 5736.68, 
##     39256.54, 3098.1, 1657.88, 6098.46, 2393.83, 698.27, 539.43, 
##     4924.74, 5696.89, 2444.83, 1547.28, 3314.93, 2109.62, 2597.38, 
##     2568.79, 2568.79, 4157.23, 2240.12, 2904.57, 17596.38, 22300.21, 
##     6939.57, 1736.68, 1314.2, 2429.21, 1374.35, 21026.39, 9310.18, 
##     227111.93, 5620.8, 2080.25, 15144.97, 20161.66, 1191.74, 
##     3668.3, 3783.8, 6192.41, 158794.92, 19172.73, 8720.92, 207097.89, 
##     2100.08, 2423.16, 2848.75, 6338.75, 9212.51, 4446.2, 2761.28, 
##     908.08, 26647.84, 3513.95, 4531.74, 2177.64, 4351.49, 57019.94, 
##     1316.78, 6684.46, 2991.24, 1426.06, 1315.87, 16281.27, 3537.83, 
##     5260.27, 9320.38, 9695.33, 34462.6, 4986.07, 49503.87, 8141.77, 
##     610.57, 31735.98, 9847.8, 2524.25, 2934.14, 9278.9, 1516.05, 
##     19681.54, 21105.1, 6177, 2630.14, 4733.94, 1442.12, 5230.4, 
##     62271.21), c(12.8, 7.9, 4.9, 5.8, 8.2, 8, 13.4, 12.9, 4.6, 
##     17.7, 5.4, 20.6, 4.9, 6.4, 5.7, 6.8, 7.4, 6.2, 5.4, 19.9, 
##     5.6, 4.7, 6.5, 9.5, 6.4, 48.6, 5.9, 5.3, 15.5, 23.1, 6.4, 
##     6.9, 7.9, 5.3, 14.4, 11.1, 9, 10.5, 7.2, 7.4, 9, 6.9, 11.7, 
##     6, 16.4, 8.9, 13.4, 6.2, 4.9, 6.2, 5.7, 8.3, 15.5, 4, 20.8, 
##     4.8, 6.6, 7.2, 9.1, 7.4, 5.9, 29.1, 8.3, 6.3, 3.7, 10.9, 
##     15.3, 8.5, 3.3, 7.3, 7, 6.9, 10.1, 15.2, 20.5, 7.4, 5.2, 
##     7.4, 12, 5.3, 7.5, 5.2, 5.2, 11.5, 9.4, 13.2, 20.3, 7.6, 
##     27.4, 4.9, 7.7, 7.8, 30.5, 11.2, 5.3, 13.9, 7.8, 5.2, 4.8, 
##     7.4, 15.7, 5.7, 7.2, 5.4, 7.5, 5.3, 8.3, 5, 9.1, 8.2, 10, 
##     9.8, 7.1, 14.9, 19.3, 6.5, 11.4, 8.2, 8, 5.5, 9.7, 5.9, 14.4, 
##     15.7, 12, 8.8, 5.9, 6.6, 6.2, 18.6), c(2, 1.5, 1.6, 1.6, 
##     1.3, 2, 1.8, 1.2, 1.6, 2, 1.7, 1.5, 1.6, 1.8, 1.5, 1.7, 1.8, 
##     1.7, 2.1, 2.2, 1.5, 1.5, 1.6, 1.8, 1.7, 1.9, 1.7, 1.4, 1.7, 
##     1.7, 1.6, 1.6, 1.7, 1.5, 1.6, 1.4, 1.7, 1.8, 1.5, 1.6, 1.4, 
##     1.3, 1.6, 1.7, 1.7, 2, 1.6, 1.8, 1.8, 1.6, 1.6, 1.7, 1.8, 
##     2.1, 3, 1.6, 1.7, 1.5, 1.7, 1.5, 1.5, 1.7, 1.6, 1.5, 2.1, 
##     1.4, 1.4, 1.6, 1.9, 1.5, 1.6, 1.5, 1.9, 1.6, 1.8, 1.6, 1.8, 
##     1.9, 1.8, 1.8, 1.4, 1.7, 1.6, 1.8, 1.5, 2.2, 2.1, 1.5, 1.5, 
##     1.6, 1.6, 1.8, 1.4, 1.8, 1.8, 1.9, 1.5, 1.7, 1.7, 1.8, 1.5, 
##     1.6, 1.9, 1.8, 1.5, 1.7, 1.7, 1.8, 1.6, 1.6, 1.7, 2, 1.4, 
##     1.7, 1.8, 1.6, 1.6, 1.6, 1.5, 1.5, 1.5, 1.6, 1.6, 1.4, 1.6, 
##     1.5, 1.7, 1.5, 1.6, 2)), control = list(20, 7, 0, 4, 5, 2, 
##     0, 30, 0))
##   n= 130 
## 
##           CP nsplit rel error
## 1 0.21392330      0 1.0000000
## 2 0.03974681      1 0.7860767
## 3 0.03787457      2 0.7463299
## 4 0.03377736      3 0.7084553
## 
## Variable importance
##         pib         pop        icms         ipi         vab        ipva 
##          20          16          16          16          15          13 
## pop_ocu_per 
##           3 
## 
## Node number 1: 130 observations,    complexity param=0.2139233
##   mean=1.676154, MSE=0.04920059 
##   left son=2 (114 obs) right son=3 (16 obs)
##   Primary splits:
##       pib         < 1152899000 to the left,  improve=0.2139233, (0 missing)
##       ipi         < 558.5      to the left,  improve=0.2120691, (0 missing)
##       icms        < 138212.8   to the left,  improve=0.2120691, (0 missing)
##       vab         < 317666500  to the left,  improve=0.1385581, (0 missing)
##       pop_ocu_per < 17.05      to the left,  improve=0.1055147, (0 missing)
##   Surrogate splits:
##       pop  < 75754.5    to the left,  agree=0.977, adj=0.812, (0 split)
##       icms < 100085.4   to the left,  agree=0.977, adj=0.812, (0 split)
##       ipi  < 405.08     to the left,  agree=0.977, adj=0.812, (0 split)
##       vab  < 457877600  to the left,  agree=0.969, adj=0.750, (0 split)
##       ipva < 27413.98   to the left,  agree=0.954, adj=0.625, (0 split)
## 
## Node number 2: 114 observations,    complexity param=0.03974681
##   mean=1.637719, MSE=0.02848954 
##   left son=4 (90 obs) right son=5 (24 obs)
##   Primary splits:
##       pop_ocu_per < 5.45       to the right, improve=0.07827549, (0 missing)
##       pop         < 57558.5    to the left,  improve=0.06007467, (0 missing)
##       pib         < 355729000  to the left,  improve=0.04534830, (0 missing)
##       icms        < 38139.45   to the left,  improve=0.03824473, (0 missing)
##       ipi         < 154.31     to the left,  improve=0.03824473, (0 missing)
## 
## Node number 3: 16 observations
##   mean=1.95, MSE=0.11125 
## 
## Node number 4: 90 observations,    complexity param=0.03787457
##   mean=1.613333, MSE=0.02448889 
##   left son=8 (68 obs) right son=9 (22 obs)
##   Primary splits:
##       pib  < 447616800  to the left,  improve=0.10991320, (0 missing)
##       vab  < 108727000  to the left,  improve=0.10106620, (0 missing)
##       pop  < 61064.5    to the left,  improve=0.09177478, (0 missing)
##       icms < 39233.84   to the left,  improve=0.07276027, (0 missing)
##       ipi  < 158.695    to the left,  improve=0.07276027, (0 missing)
##   Surrogate splits:
##       pop  < 41344.5    to the left,  agree=0.956, adj=0.818, (0 split)
##       vab  < 108727000  to the left,  agree=0.956, adj=0.818, (0 split)
##       icms < 37221.12   to the left,  agree=0.944, adj=0.773, (0 split)
##       ipi  < 152.04     to the left,  agree=0.944, adj=0.773, (0 split)
##       ipva < 9865.655   to the left,  agree=0.922, adj=0.682, (0 split)
## 
## Node number 5: 24 observations
##   mean=1.729167, MSE=0.03289931 
## 
## Node number 8: 68 observations
##   mean=1.583824, MSE=0.01929715 
## 
## Node number 9: 22 observations
##   mean=1.704545, MSE=0.02952479

## note: only 6 unique complexity parameters in default grid. Truncating the grid to 6 .
## 
## Aggregating results
## Selecting tuning parameters
## Fitting mtry = 3 on full training set

## Aggregating results
## Selecting tuning parameters
## Fitting mstop = 50, prune = no on full training set

## Boosted Generalized Linear Model 
## 
## 130 samples
##   7 predictor
## 
## No pre-processing
## Resampling: Cross-Validated (10 fold) 
## Summary of sample sizes: 116, 117, 117, 118, 117, 118, ... 
## Resampling results across tuning parameters:
## 
##   mstop  RMSE       Rsquared   MAE      
##     50   0.2042264  0.1831534  0.1529216
##    100   0.2095338  0.2060137  0.1536587
##    150   0.2150620  0.2170094  0.1546601
##    200   0.2204597  0.2215629  0.1560612
##    250   0.2252353  0.2250923  0.1571951
##    300   0.2297131  0.2284649  0.1583479
##    350   0.2337211  0.2312378  0.1594117
##    400   0.2372954  0.2338719  0.1603547
##    450   0.2404157  0.2357173  0.1611741
##    500   0.2432714  0.2374265  0.1619084
##    550   0.2457805  0.2396897  0.1625161
##    600   0.2480406  0.2417697  0.1630674
##    650   0.2500663  0.2433059  0.1635627
##    700   0.2505879  0.2454942  0.1635722
##    750   0.2507893  0.2467122  0.1635075
##    800   0.2510175  0.2487379  0.1634388
##    850   0.2512230  0.2505170  0.1633634
##    900   0.2513818  0.2522773  0.1632732
##    950   0.2516119  0.2536995  0.1632143
##   1000   0.2517987  0.2549045  0.1631612
## 
## Tuning parameter 'prune' was held constant at a value of no
## RMSE was used to select the optimal model using the smallest value.
## The final values used for the model were mstop = 50 and prune = no.
## 
##   Generalized Linear Models Fitted via Gradient Boosting
## 
## Call:
## (function (formula, data = list(), weights = NULL, offset = NULL,     family = Gaussian(), na.action = na.pass, contrasts.arg = NULL,     center = TRUE, control = boost_control(), oobweights = NULL,     ...) {    if (length(formula[[3]]) == 1) {        if (as.name(formula[[3]]) == ".") {            formula <- as.formula(paste(deparse(formula[[2]]),                 "~", paste(names(data)[names(data) != all.vars(formula[[2]])],                   collapse = "+"), collapse = ""))        }    }    cl <- match.call()    mf <- match.call(expand.dots = FALSE)    m <- match(c("formula", "data", "weights", "na.action"),         names(mf), 0L)    mf <- mf[c(1L, m)]    mf$drop.unused.levels <- TRUE    mf$data <- data    mf[[1L]] <- quote(stats::model.frame)    mf <- eval(mf, parent.frame())    if (!control$center) {        center <- FALSE        warning("boost_control(center = FALSE) is deprecated, use glmboost(..., center = FALSE)")    }    X <- model.matrix(attr(mf, "terms"), data = mf, contrasts.arg = contrasts.arg)    assign <- attr(X, "assign")    cm <- numeric(ncol(X))    if (center) {        if (!attr(attr(mf, "terms"), "intercept") == 1)             warning("model with centered covariates does not contain intercept")        cm <- colMeans(X, na.rm = TRUE)        cm[assign == 0] <- 0        X <- scale(X, center = cm, scale = FALSE)    }    newX <- function(newdata) {        mf <- model.frame(delete.response(attr(mf, "terms")),             data = newdata, na.action = na.action)        X <- model.matrix(delete.response(attr(mf, "terms")),             data = mf, contrasts.arg = contrasts.arg)        scale(X, center = cm, scale = FALSE)    }    bl <- list(bolscw(X))    response <- model.response(mf)    weights <- model.weights(mf)    ret <- mboost_fit(bl, response = response, weights = weights,         offset = offset, family = family, control = control,         oobweights = oobweights, ...)    ret$newX <- newX    ret$assign <- assign    ret$center <- cm    ret$call <- cl    ret$hatvalues <- function() {        H <- vector(mode = "list", length = ncol(X))        MPinv <- ret$basemodel[[1]]$MPinv()        for (j in unique(ret$xselect())) H[[j]] <- (X[, j] %*%             MPinv[j, , drop = FALSE]) * control$nu        H    }    ret$rownames <- rownames(mf)    ret$model.frame <- function(which = NULL) {        if (!is.null(which))             warning("Argument ", sQuote("which"), " is ignored")        mf    }    update <- ret$update    ret$update <- function(weights = NULL, oobweights = NULL,         risk = "oobag", trace = NULL) {        res <- update(weights = weights, oobweights = oobweights,             risk = risk, trace = trace)        res$newX <- newX        res$assign <- assign        res$center <- cm        res$call <- cl        res$hatvalues <- function() {            H <- vector(mode = "list", length = ncol(X))            MPinv <- res$basemodel[[1]]$MPinv()            for (j in unique(res$xselect())) H[[j]] <- (X[, j] %*%                 MPinv[j, , drop = FALSE]) * control$nu            H        }        res$rownames <- rownames(mf)        res$model.frame <- function(which = NULL) {            if (!is.null(which))                 warning("Argument ", sQuote("which"), " is ignored")            mf        }        class(res) <- c("glmboost", "mboost")        res    }    class(ret) <- c("glmboost", "mboost")    return(ret)})(formula = .outcome ~ ., data = "structure(list(pop = c(100346, 25065, 37082, 22984, 22870, 11226, ",     family = new("boost_family_glm", fW = function (f)     return(rep(1, length = length(f))), ngradient = function (y,         f, w = 1)     y - f, risk = function (y, f, w = 1)     sum(w * loss(y, f), na.rm = TRUE), offset = function (x,         w, ...)     UseMethod("weighted.mean"), check_y = function (y)     {        if (!is.numeric(y) || !is.null(dim(y)))             stop("response is not a numeric vector but ", sQuote("family = Gaussian()"))        y    }, weights = function (w)     {        switch(weights, any = TRUE, none = isTRUE(all.equal(unique(w),             1)), zeroone = isTRUE(all.equal(unique(w + abs(w -             1)), 1)), case = isTRUE(all.equal(unique(w - floor(w)),             0)))    }, nuisance = function ()     return(NA), response = function (f)     f, rclass = function (f)     NA, name = "Squared Error (Regression)", charloss = "(y - f)^2 \n"),     control = structure(list(mstop = 50, nu = 0.1, risk = "inbag",         stopintern = FALSE, center = TRUE, trace = FALSE), class = "boost_control"),     x = "xData", y = "yData")
## 
## 
##   Squared Error (Regression) 
## 
## Loss function: (y - f)^2 
##  
## 
## Number of boosting iterations: mstop = 50 
## Step size:  0.1 
## Offset:  1.676154 
## 
## Coefficients: 
##   (Intercept)           pib           ipi          ipva   pop_ocu_per 
## -3.739448e-02  4.124496e-11  5.400149e-05 -4.412225e-07  3.734836e-04 
## attr(,"offset")
## [1] 1.676154
## 
## Selection frequencies:
##         pib        ipva         ipi pop_ocu_per 
##        0.48        0.30        0.18        0.04
## 
## Call:
## resamples.default(x = list(LM = municipios_LM, RPART = municipios_RPART, RF
##  = municipios_RF, GLMBOOST = municipios_GLMB))
## 
## Models: LM, RPART, RF, GLMBOOST 
## Number of resamples: 10 
## Performance metrics: MAE, RMSE, Rsquared 
## Time estimates for: everything, final model fit
## 
## Call:
## summary.resamples(object = melhor_modelo)
## 
## Models: LM, RPART, RF, GLMBOOST 
## Number of resamples: 10 
## 
## MAE 
##                Min.   1st Qu.    Median      Mean   3rd Qu.      Max. NA's
## LM       0.11250426 0.1365069 0.1452234 0.1500140 0.1579638 0.2074719    0
## RPART    0.10031459 0.1271278 0.1359506 0.1528806 0.1666096 0.2361921    0
## RF       0.09966572 0.1374797 0.1488906 0.1504210 0.1745556 0.1945268    0
## GLMBOOST 0.13586466 0.1392127 0.1483762 0.1529216 0.1580298 0.2040739    0
## 
## RMSE 
##               Min.   1st Qu.    Median      Mean   3rd Qu.      Max. NA's
## LM       0.1403318 0.1668707 0.1823795 0.1977971 0.1945958 0.3738177    0
## RPART    0.1323618 0.1592376 0.1846160 0.2019010 0.2068152 0.3577999    0
## RF       0.1309655 0.1629643 0.1899645 0.2008156 0.2271987 0.3318397    0
## GLMBOOST 0.1622121 0.1732080 0.1880893 0.2042264 0.2056509 0.3643783    0
## 
## Rsquared 
##                  Min.    1st Qu.     Median      Mean   3rd Qu.      Max. NA's
## LM       0.0009112821 0.02468258 0.09255976 0.1828879 0.3366001 0.5605766    0
## RPART    0.0164734594 0.05615441 0.22041347 0.1906448 0.2755084 0.4544527    0
## RF       0.0028397725 0.02329336 0.09198198 0.1797962 0.3578080 0.4839327    0
## GLMBOOST 0.0275083393 0.04190270 0.17261687 0.1831534 0.2404946 0.6005926    0